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Identification technology of apple diseases is of great significance in improving production efficiency and quality. This paper has used apple Alternaria blotch and brown spot disease leaves as the research object and proposes a disease spot segmentation and disease identification method based on DFL-UNet+CBAM to address the problems of low recognition accuracy and poor performance of small spot segmentation in apple leaf disease recognition. The goal of this paper is to accurately prevent and control apple diseases, avoid fruit quality degradation and yield reduction, and reduce the resulting economic losses. DFL-UNet +CBAM model has employed a hybrid loss function of Dice Loss and Focal Loss as the loss function and added CBAM attention mechanism to both effective feature layers extracted by the backbone network and the results of the first upsampling, enhancing the model to rescale the inter-feature weighting relationships, enhance the channel features of leaf disease spots and suppressing the channel features of healthy parts of the leaf, and improving the network's ability to extract disease features while also increasing model robustness. In general, after training, the average loss rate of the improved model decreases from 0.063 to 0.008 under the premise of ensuring the accuracy of image segmentation. The smaller the loss value is, the better the model is. In the lesion segmentation and disease identification test, MIoU was 91.07%, MPA was 95.58%, F1 Score was 95.16%, MIoU index increased by 1.96%, predicted disease area and actual disease area overlap increased, MPA increased by 1.06%, predicted category correctness increased, F1 Score increased by 1.14%, the number of correctly identified lesion pixels increased, and the segmentation result was more accurate. Specifically, compared to the original U-Net model, the segmentation of Alternaria blotch disease, the MIoU value increased by 4.41%, the MPA value increased by 4.13%, the Precision increased by 1.49%, the Recall increased by 4.13%, and the F1 Score increased by 2.81%; in the segmentation of brown spots, MIoU values increased by 1.18%, MPA values by 0.6%, Precision by 0.78%, Recall by 0.6%, and F1 Score by 0.69%. The spot diameter of the Alternaria blotch disease is 0.2-0.3cm in the early stage, 0.5-0.6cm in the middle and late stages, and the spot diameter of the brown spot disease is 0.3-3cm. Obviously, brown spot spots are larger than Alternaria blotch spots. The segmentation performance of smaller disease spots has increased more noticeably, according to the quantitative analysis results, proving that the model's capacity to segment smaller disease spots has greatly improved. The findings demonstrate that for the detection of apple leaf diseases, the method suggested in this research has a greater recognition accuracy and better segmentation performance. The model in this paper can obtain more sophisticated semantic information in comparison to the traditional U-Net, further enhance the recognition accuracy and segmentation performance of apple leaf spots, and address the issues of low accuracy and low efficiency of conventional disease recognition methods as well as the challenging convergence of conventional deep convolutional networks. [ABSTRACT FROM AUTHOR]

Introduction and purpose: Apples are a widely consumed fruit globally and are known to be a great source of nutrition. Studies have shown that apples contain high levels of antioxidants, dietary fiber, and vitamins that contribute to numerous health benefits. This paper aims to provide a comprehensive review of the impact of apples on human health. State of knowledge: The paper reviews the current literature on the health benefits of apples, focusing on their impact on various health conditions such as cancer, cardiovascular diseases, diabetes, and obesity. Additionally, it examines the role of apples in improving intestinal, immune, and cognitive function. The paper also discusses the potential mechanisms by which apples exert their health benefits, such as high levels of flavonoids, polyphenols, and dietary fiber. Summary: In conclusion, the vast body of evidence reviewed in this paper supports the notion that consuming apples regularly can contribute to better health outcomes. They may help reduce the risk of chronic diseases, such as cancer, cardiovascular diseases, and diabetes, as well as promote healthy gut microbiota and immune function. Despite the controversy surrounding the use of pesticides in apple cultivation, the benefits of consuming apples outweigh the risks. Therefore, it is highly recommended to include apples as part of a balanced and nutritious diet to promote optimal health and wellbeing. As the saying goes, "An apple a day keeps the doctor away". [ABSTRACT FROM AUTHOR]

Apples are susceptible to infection by various pathogens during growth, which induces various leaf diseases and thus affects apple quality and yield. The timely and accurate identification of apple leaf diseases is essential to ensure the high-quality development of the apple industry. In practical applications in orchards, the complex background in which apple leaves are located poses certain difficulties for the identification of leaf diseases. Therefore, this paper suggests a novel approach to identifying and classifying apple leaf diseases in complex backgrounds. First, we used a bilateral filter-based MSRCR algorithm (BF-MSRCR) to pre-process the images, aiming to highlight the color and texture features of leaves and to reduce the difficulty of extracting leaf disease features with subsequent networks. Then, BAM-Net, with ConvNext-T as the backbone network, was designed to achieve an accurate classification of apple leaf diseases. In this network, we used the aggregate coordinate attention mechanism (ACAM) to strengthen the network's attention to disease feature regions and to suppress the interference of redundant background information. Then, the multi-scale feature refinement module (MFRM) was used to further identify deeper disease features and to improve the network's ability to discriminate between similar disease features. In our self-made complex background apple leaf disease dataset, the proposed method achieved 95.64% accuracy, 95.62% precision, 95.89% recall, and a 95.25% F1-score. Compared with existing methods, BAM-Net has higher disease recognition accuracy and classification results. It is worth mentioning that BAM-Net still performs well when applied to the task of the leaf disease identification of other crops in the PlantVillage public dataset. This indicates that BAM-Net has good generalization ability. Therefore, the method proposed in this paper can be helpful for apple disease control in modern agriculture, and it also provides a new reference for the disease identification of other crops. [ABSTRACT FROM AUTHOR]

To solve the problem that the robot often collides with the obstacles such as branches around the fruit during picking due to its inability to adapt to the fruit growing environment, this paper proposes an apple-picking robot picking path planning algorithm based on the improved PSO. The main contents of the algorithm are: firstly, the fruit and its surrounding branches are extracted from the 3D point cloud data, and the picking direction of the fruit is calculated; then the point cloud on the surface of the fruit and branches is used to establish the spatial model of obstacles; finally, an improved particle swarm optimization (PSO) algorithm is proposed to plan the obstacle avoidance trajectory of the end-effector in space, which can dynamically adjust the velocity weights according to the trend of the particle fitness value and the position of the particle swarm center of mass. The experimental results show that the improved PSO has faster convergence speed than the standard PSO, and the path planning method proposed in this paper improves the fruit-picking success rate to 85.93% and reduces the picking cycle to 12 s. This algorithm can effectively reduce the collision between the manipulator and branches during apple picking and improve the picking success rate and picking efficiency. [ABSTRACT FROM AUTHOR]

The fruit sector in Romania and, particularly, the apple production sector, is considered to have been in decline in recent decades. Changes in the behavior and consumption habits of the population, together with climate change and the increase in fruit imports, have caused shortages in this sector. Apples are regarded as a national fruit in Romania, but recently, there have been significant transformations in apple production and consumption. In order to practically observe the recorded information published in a database for this sector, we developed a questionnaire, which was completed by people in Romania. The aim of this paper was to identify the types of apple consumer profiles in Romania. The reason for choosing this specific fruit was linked to the investigated objective of the projects to which this article is related. Regarding the approached methodology, the paper focused on analyzing descriptive statistics, frequencies and clustering, by which means we designed a pattern of the apple consumer profiles in Romania. Furthermore, the profiles of respondents from two regions of Romania (South-Muntenia and South-East Regions) were explored in more detail. The findings of this research indicate a young, educated consumer profile and a distinct pattern revealed by the three clusters from the specific regions. [ABSTRACT FROM AUTHOR]

Apple grading is an essential part of the apple marketing process to achieve high profits. In this paper, an improved YOLOv5 apple grading method is proposed to address the problems of low grading accuracy and slow grading speed in the apple grading process and is experimentally verified by the designed automatic apple grading machine. Firstly, the Mish activation function is used instead of the original YOLOv5 activation function, which allows the apple feature information to flow in the deep network and improves the generalization ability of the model. Secondly, the distance intersection overUnion loss function (DIoU_Loss) is used to speed up the border regression rate and improve the model convergence speed. In order to refine the model to focus on apple feature information, a channel attention module (Squeeze Excitation) was added to the YOLOv5 backbone network to enhance information propagation between features and improve the model's ability to extract fruit features. The experimental results show that the improved YOLOv5 algorithm achieves an average accuracy of 90.6% for apple grading under the test set, which is 14.8%, 11.1%, and 3.7% better than the SSD, YOLOv4, and YOLOv5s models, respectively, with a real-time grading frame rate of 59.63 FPS. Finally, the improved YOLOv5 apple grading algorithm is experimentally validated on the developed apple auto-grader. The improved YOLOv5 apple grading algorithm was experimentally validated on the developed apple auto grader. The experimental results showed that the grading accuracy of the automatic apple grader reached 93%, and the grading speed was four apples/sec, indicating that this method has a high grading speed and accuracy for apples, which is of practical significance for advancing the development of automatic apple grading. [ABSTRACT FROM AUTHOR]

The semantic segmentation of apples from images plays an important role in the automation of the apple industry. However, existing semantic segmentation methods such as FCN and UNet have the disadvantages of a low speed and accuracy for the segmentation of apple images with complex backgrounds or rotten parts. In view of these problems, a network segmentation model based on deep learning, DeepMDSCBA, is proposed in this paper. The model is based on the DeepLabV3+ structure, and a lightweight MobileNet module is used in the encoder for the extraction of features, which can reduce the amount of parameter calculations and the memory requirements. Instead of ordinary convolution, depthwise separable convolution is used in DeepMDSCBA to reduce the number of parameters to improve the calculation speed. In the feature extraction module and the cavity space pyramid pooling module of DeepMDSCBA, a Convolutional Block Attention module is added to filter background information in order to reduce the loss of the edge detail information of apples in images, improve the accuracy of feature extraction, and effectively reduce the loss of feature details and deep information. This paper also explored the effects of rot degree, rot position, apple variety, and background complexity on the semantic segmentation performance of apple images, and then it verified the robustness of the method. The experimental results showed that the PA of this model could reach 95.3% and the MIoU could reach 87.1%, which were improved by 3.4% and 3.1% compared with DeepLabV3+, respectively, and superior to those of other semantic segmentation networks such as UNet and PSPNet. In addition, the DeepMDSCBA model proposed in this paper was shown to have a better performance than the other considered methods under different factors such as the degree or position of rotten parts, apple varieties, and complex backgrounds. [ABSTRACT FROM AUTHOR]

This paper presents some features of apple production in Norway, the northernmost apple-growing country in the world. Acceptable growing conditions prevail along the fjords in western Norway and around the lakes in eastern Norway at 60° north. These specific mesic climate conditions are associated with very long summer days (18 h daylight mid-summer) and short winter days (6 h daylight), with frost rarely occurring in the spring along the fjord areas. The present apple-growing technique in Norway is similar to that of other developed apple-growing countries, taking into account that all local growing phases involve a considerable delay in progress (1.5–2 months). Therefore, high-density planting systems based on the use of dwarf rootstocks (mainly M.9) with imported early maturing international apple cultivars are used in most orchards. The most common soil type has high organic matter content (2–18%), which persists due to the cool climate and low mineralization, and a clay content of <15%, which results from the formation of the soil from bedrock. The increase in average temperatures caused by current climatic changes leads to a complex combination of different physiological effects on apples, which can have positive or negative effects on the phenology of the trees. The main advantage of Norwegian apple production is that the quality and aroma of the fruit meet the current demands of the local market. [ABSTRACT FROM AUTHOR]

Machine Learning (ML) has enabled many image-based object detection and recognition-based solutions in various fields and is the state-of-the-art method for these tasks currently. Therefore, it is of interest to apply this technique to different questions. In this paper, we explore whether it is possible to classify apple cultivars based on fruits using ML methods and images of the apple in question. The goal is to develop a tool that is able to classify the cultivar based on images that could be used in the field. This helps to draw attention to the variety and diversity in fruit growing and to contribute to its preservation. Classifying apple cultivars is a certain challenge in itself, as all apples are similar, while the variety within one class can be high. At the same time, there are potentially thousands of cultivars indicating that the task becomes more challenging when more cultivars are added to the dataset. Therefore, the first question is whether a ML approach can extract enough information to correctly classify the apples. In this paper, we focus on the technical requirements and prerequisites to verify whether ML approaches are able to fulfill this task with a limited number of cultivars as proof of concept. We apply transfer learning on popular image processing convolutional neural networks (CNNs) by retraining them on a custom apple dataset. Afterward, we analyze the classification results as well as possible problems. Our results show that apple cultivars can be classified correctly, but the system design requires some extra considerations. [ABSTRACT FROM AUTHOR]

Zhaotong City in Yunnan Province is one of the largest apple growing bases in China. However, the terrain of Zhaotong City is complicated, and the rainy weather is more frequent, which brings difficulties to the identification of apple orchards by remote sensing. In this paper, an improved spectral angle feature is proposed by combining the Spectral Angle Mapper and Sentinel-1 data. Based on the Google Earth Engine and Sentinel image, a random forest classifier was used to extract apple orchards in the Ganhe Reservoir area, Zhaoyang District, Zhaotong City, which provides a theoretical basis for extracting the spatial distribution and sustainable development of the local apple industry. The classification results show that the improved spectral angle characteristics can improve the overall accuracy and F1 score of apple orchards. The RGB band combined with NDVI, GLCM, and improved spectral angle features obtained the most favorable results, and the F1 score and overall accuracy were 88.89% and 84.44%, respectively, which proved the reliability of the method in identifying apple orchards in Zhaotong City. [ABSTRACT FROM AUTHOR]

Copyright of Ekonomika is the property of Society of Economists 'Ekonomika' and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to improve the performance of the automatic apple grading and sorting system, in this paper, an ensemble model of ordinal classification based on neural network with ordered partitions and Dempster–Shafer theory is proposed. As a non‐destructive grading method, apples are graded into three grades based on the Soluble Solids Content value, with features extracted from the preprocessed near‐infrared spectrum of apple serving as model inputs. Considering the uncertainty in grading labels, mass generation approach and evidential encoding scheme for ordinal label are proposed, with uncertainty handled within the framework of Dempster–Shafer theory. Constructing neural network with ordered partitions as the base learner, the learning procedure of the Bagging‐based ensemble model is detailed. Experiments on Yantai Red Fuji apples demonstrate the satisfactory grading performances of proposed evidential ensemble model for ordinal classification. [ABSTRACT FROM AUTHOR]

In this paper, 317 literature in the Web of Science (WoS) related to research on apple by near‐infrared spectroscopy (NIRS) were drawn on the knowledge map of the number of literature, the co‐occurrence network of authors and institutions, the co‐occurrence and clustering of keywords based on CiteSpace. And a related analysis was carried out. Combined with the results of visual analysis and related literature, the research hotspots were sorted out and discussed. This paper provides a certain reference for relevant researchers to study in this field and provides a new method for macroscopically grasping the current status of apple quality detection research, which helps new researchers to quickly integrate into this field and obtain more valuable scientific information. [ABSTRACT FROM AUTHOR]

Copyright of Guangdong Agricultural Sciences is the property of South China Agricultural University, Guangdong Academy of Agricultural Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Spatial-frequency domain imaging (SFDI) has been developed as an emerging modality for detecting early-stage bruises of fruits, such as apples, due to its unique advantage of a depth-resolved imaging feature. This paper presents theoretical and experimental analyses to determine the light penetration depth in apple tissues under spatially modulated illumination. Simulation and practical experiments were then carried out to explore the maximum light penetration depths in 'Golden Delicious' apples. Then, apple experiments for early-stage bruise detection using the estimated reduced scattering coefficient mapping were conducted to validate the results of light penetration depths. The results showed that the simulations produced comparable or a little larger light penetration depth in apple tissues (~2.2 mm) than the practical experiment (~1.8 mm or ~2.3 mm). Apple peel further decreased the light penetration depth due to the high absorption properties of pigment contents. Apple bruises located beneath the surface peel with the depth of about 0–1.2 mm could be effectively detected by the SFDI technique. This study, to our knowledge, made the first effort to investigate the light penetration depth in apple tissues by SFDI, which would provide useful information for enhanced detection of early-stage apple bruising by selecting the appropriate spatial frequency. [ABSTRACT FROM AUTHOR]

Orchard systems have drastically changed over the last three decades to high-density plantings that prioritize light interception that is evenly distributed throughout the entire canopy. These conditions allow the production of fruit with a high red color that meets consumer demands for uniformly colored fruit without external disorders. However, these systems also expose a higher proportion of fruit to full-sunlight conditions. In many semi-arid apple production regions, summer temperatures often exceed thresholds for the development of fruit sunburn, which can lead to >10% fruit losses in some regions and some years. To combat this, growers and researchers use sunburn mitigation strategies such as shade netting and evaporative cooling, which bring a different set of potential fruit quality impacts. Often, there is a tradeoff between red color development and fruit sunburn, particularly for strategies that affect light intensity reaching the fruit surface. In this paper, we review agronomic and environmental factors leading to reductions in red color and increases in sunburn incidence, along with advancements in management practices that help mitigate these issues. Furthermore, we also identify gaps in knowledge on the influence climate change might have on the viability of some practices that either enhance red color or limit sunburn for apple orchards in semi-arid environments. There is a need for cost-effective management strategies that reduce losses to sunburn but do not inhibit red color development in bicolor apple cultivars. [ABSTRACT FROM AUTHOR]

The yield of the crop is a complex function of a number of dependent traits, which makes yield prediction a statistically difficult task. A number of work on yield prediction using morphological characters already exists in the literature. Most of the work used statistical techniques such as linear regression and crop yield models, which assume a linear relationship between yield and the morphological traits; in actual practice, such a linear relationship is seldom achieved. With the advancement in the field of machine learning techniques, these methods can provide a viable alternative for dealing with nonlinear relationships for yield prediction. Globally, apples are the most consumed fruit. In this paper, attempts have been made to predict the yield of the apple crop using morphological traits. PCA was used for selection of the significant variables. These variables were later used as input variables in the ANN model with different hidden layers for predicting crop yield. The predictive performance of the model was evaluated using standard statistical tests. Sensitivity analysis was performed to find out the individual effects of each character on the apple yield. The study contributes to a better understanding of the complex relationships between crop yield and morphological traits. [ABSTRACT FROM AUTHOR]

Disease has always been one of the main reasons for the decline of apple quality and yield, which directly harms the development of agricultural economy. Therefore, precise diagnosis of apple diseases and correct decision making are important measures to reduce agricultural losses and promote economic growth. In this paper, a novel Multi-scale Dense classification network is adopted to realize the diagnosis of 11 types of images, including healthy and diseased apple fruits and leaves. The diagnosis of different kinds of diseases and the same disease with different grades was accomplished. First of all, to solve the problem of insufficient images of anthracnose and ring rot, Cycle-GAN algorithm was applied to achieve dataset expansion on the basis of traditional image augmentation methods. Cycle-GAN learned the image characteristics of healthy apples and diseased apples to generate anthracnose and ring rot lesions on the surface of healthy apple fruits. The diseased apple images generated by Cycle-GAN were added to the training set, which improved the diagnosis performance compared with other traditional image augmentation methods. Subsequently, DenseNet and Multi-scale connection were adopted to establish two kinds of models, Multi-scale Dense Inception-V4 and Multi-scale Dense Inception-Resnet-V2, which facilitated the reuse of image features of the bottom layers in the classification neural networks. Both models accomplished the diagnosis of 11 different types of images. The classification accuracy was 94.31 and 94.74%, respectively, which exceeded DenseNet-121 network and reached the state-of-the-art level. [ABSTRACT FROM AUTHOR]

Fuji apples are one of the most important and popular economic crops worldwide in the fruit industry. Nowadays, there is a huge imbalance between the urgent demand of precise automated sorting models of fruit ripeness grades due to the increasing consumption levels and the limitations of most existing methods. In this regard, this paper presents a novel CNN-based fine-grained lightweight architecture for the task of Fuji apple maturity classification (FGAL-MC). Our proposed FGAL-MC architecture has three advantages compared with related previous research works. Firstly, we established a novel Fuji apple maturity dataset. We investigated the Fuji apple's different growth stages using image samples that were captured in open-world orchard environments, which have the benefit of being able to guide the related methods to be more suitable for the practical working environment. Secondly, because maturity grades are difficult to discriminate due to the issues of subtle expression differences, as well as the various challenging disadvantages for the unstructured surroundings, we designed our network as a fine-grained classification architecture by introducing an attention mechanism to learn class-specific regions and discrimination. Thirdly, because the number of parameters of an architecture determines the time-cost and hardware configuration to some extent, we designed our proposed architecture as a lightweight structure, which is able to be applied or promoted for actual agriculture field operations. Finally, comprehensive qualitative and quantitative experiments demonstrated that our presented method can achieve competitive results in terms of accuracy, precision, recall, F1-score, and time-cost. In addition, extensive experiments indicated our proposed method also has outstanding performance in terms of generalization ability. [ABSTRACT FROM AUTHOR]

Post-harvest diseases are one of the main causes of economical losses in the apple fruit production sector. Therefore, this paper presents an application of a knowledge-based expert system to diagnose post-harvest diseases of apple. Specifically, we detail the process of domain knowledge elicitation for constructing a Bayesian network reasoning system. We describe the developed expert system, dubbed BN-DSSApple, and the diagnostic mechanism given the evidence provided by the user, as well as a likelihood evidence method, learned from the estimated consensus of users' and expert's interactions, to effectively transfer the performance of the model to different cohorts of users. Finally, we detail a novel technique for explaining the provided diagnosis, thus increasing the trust in the system. We evaluate BN-DSSApple with three different types of user studies, involving real diseased apples, where the ground truth of the target instances was established by microbiological and DNA analysis. The experiments demonstrate the performance differences in the knowledge-based reasoning mechanism due to heterogeneous users interacting with the system under various conditions and the capability of the likelihood-based method to improve the diagnostic performance in different environments. [ABSTRACT FROM AUTHOR]

Apple is one of the most common fruit crops in the Russian fruit-growing industry, with huge varietal diversity and a vast cultivation area. The key regions for industrial-scale apple cultivation are the Central, Central Chernozem, and North Caucasian Districts. The main disadvantage of the relevant apple cultivars, especially the ones intended for intensified horticultural practices, is their low resistance against abiotic stresses and the fruit's low quality and poor marketable condition. In Russia, apple is a crop of strategic importance that is consistently included in the household food basket, so fruit producers hold new varieties to higher standards and expect them to outperform their predecessors in terms of yield per plant, resistance against abiotic and biotic stresses, and quality, as well as show strong competitiveness and a more rapid return on investment, while satisfying stricter requirements. The objective of the present study was to summarize the data on the phenotypic manifestations of economically valuable traits of the apple cultivars approved for use in the Russian Federation depending on the region of cultivation; to determine the parametric characteristics of the most valuable traits in the form of a model of an "ideal" regionally adapted industrial cultivar, and to identify the sources of the traits in the regions suitable for their production. A regionally adapted model of commercial apple cultivars, characterized by 28 features and properties divided into three groups and defining the cultivar's resistance against abiotic and biotic stresses, yield per plant, product quality, and suitability for mechanized harvesting, is presented in this paper. In the European part of Russia, the optimal parameters of a commercial apple tree cultivar are as follows: plant height on a medium-sized rootstock under 3 m; potential yield per plant of at least 25–50 kg; high fruit uniformity above 80%; winter and late-winter harvest maturity period; high storability of over 210 days and good transportability; average fruit mass from 120 g to 220 g; juicy and shattering crisp pulp; small seed cavity; fragrant fruits with taste rating of at least 4.5 points; appearance rating of 5 points and attractive, mostly red, glossy color with natural wax bloom; regular, symmetric, but diverse shapes; content of sugar above 10%, ascorbic acid above 15 mg/100 g, organic acids up to 1% (for dessert varieties); content of soluble dry solids of at least 20%. The cultivars that come closest to the regionally adapted model of an ideal variety based on the set of features discussed are as follows: Feya, Soyuz, Orfej, Margo, Sirius, Noktyurn, Vasilisa Karmen, Florina, Dayton, Early Mac, Gala and Gala Schniga in the North Caucasian District; Svezhest', Orlovskoe Poles'e, Aprel'skoe, Ven'yaminovskoe, Bolotovskoe, Vympel, Uspenskoe, Fregat, Bylina, Flagman, and Akademik Kazakov in the North Caucasian District; and varieties Imrus, Mayak Zagor'ya, and Bolotovskoe in the Central District. These cultivars are characterized by high resistance against weather anomalies, scab immunity, high yields, marketable quality, and storability. In addition, in southern regions, a prolonged bloom period acts as a protective adaptive response to low-temperature stress. [ABSTRACT FROM AUTHOR]

For the purpose of minimizing the damage to apples during the operation of an apple-picking robot, improving the stability of an apple-harvesting mechanical hand and reducing the use and operation cost of the robot, a novel biomimetic apple mechanical hand based on negative pressure air suction is purposely designed based on the physical characteristics of apples by imitating octopus predation. The mechanical hand has four suction cups evenly distributed in the hand, which produce concave deformation under negative vacuum pressure to fit the apple surface and adsorb the apple; the fruit stalk is separated by wrist rotation and dragging in a compound way to complete the picking operation. This paper firstly determines the key parameters of the mechanical hand based on the characteristics of silicone material and the three growth postures of apples, tries to make a physical prototype of the mechanical hand and conducts relevant performance tests on the picking platform built in the laboratory to explore the best combination of parameters and further optimize the manipulator. The results show that the success rate of the biomimetic nondestructive apple picker with the best combination of parameters is 100%, 76% and 68% for three different apple-growth postures and the operation is smooth, reliable, safe and non-invasive. [ABSTRACT FROM AUTHOR]

Research into autonomous (robotic) apple picking has not yet resolved the problem of finding the optimal picking orientation. Robotic picking efficiency, in terms of picking all available apples without loss or damage, remains low. This paper proposes a method of determining the optimal picking orientation relative to the target fruit and adjacent branches from the point cloud of the apple and the surrounding space. The picking mechanism is then able to avoid branches and accurately grasp the target apple in order to pick it. The apple is first identified by the YOLOv3 target detection algorithm, and a point cloud of the fruit and the space surrounding it is obtained. The random sample consensus algorithm RANSAC is used for sphere fitting, and the fruit is idealized as a sphere. RANSAC also idealizes the branch as a line that is fitted to the branch bearing the target apple in the point cloud around it. The distance between the line of the branch and the fruit centroid is constrained in fitting to ensure identification of the branch/line closest to the apple/sphere. The best apple picking orientation is determined from the positional relationship between the straight branch/line and the center of the apple/sphere. The performance of the algorithm was evaluated using apples with various orientations on growing trees. The average angle error between the calculated picking direction vector and the expected direction vector was 11.81°, and the standard deviation was 13.65°; 62.658% of the determinations erred by ≤10°, and 85.021% erred by ≤20°. The average time for estimating the orientation of an apple was 0.543 s. The accuracy and speed of the algorithm enabled the robotic picker to operate at a speed that matches that of a human apple picker. [ABSTRACT FROM AUTHOR]

This paper aimed to analyze the variability of spring phenophases in the apple species, the 'GoldRush' cultivar to analyze the risk of spring frost for apple production in Stăneti area of Arge County (45°13'44N, 24°50'9E, altitude 540m), Romania, and the role of phenological acclimatization. The timing of phenological stages is made according to the definitions of the Biologische Bundesanstalt, Bundessortenamt, and CHemical industry (BBCH). Based on Julian day number, on average for 4 years, the phenophases occurred from the 85.54 day of year, marked by "beginning of leaf bud swelling/inflorescence bud swelling" (BBCH 01/51), until 1the 23.36 day of year for phenophase "end of flowering" (BBCH 69). Analyzing the data obtained, they allowed us to assess the temporal trends of development of budding and flowering phenophases and the trends of risks in the light of global warming. The research will be continued in order to analyze a longer period of time and to analyze different cultivars, in order to achieve sustainable and competitive fruit crops. [ABSTRACT FROM AUTHOR]

Apple is one of the most important economic fruit crops in the world. Despite all the strategies of integrated pest management (IPM), insecticides are still frequently used in its cultivation. In addition, pest phenology is extremely influenced by changing climatic conditions. The frequent spread of invasive species, unexpected pest outbreaks, and the development of additional generations are some of the problems posed by climate change. The adopted strategies of IPM therefore need to be changed as do the current monitoring techniques, which are increasingly unreliable and outdated. The need for more sophisticated, accurate, and efficient monitoring techniques is leading to increasing development of automated pest monitoring systems. In this paper, we summarize the automatic methods (image analysis systems, smart traps, sensors, decision support systems, etc.) used to monitor the major pest in apple production (Cydia pomonella L.) and other important apple pests (Leucoptera maifoliella Costa, Grapholita molesta Busck, Halyomorpha halys Stål, and fruit flies—Tephritidae and Drosophilidae) to improve sustainable pest management under frequently changing climatic conditions. [ABSTRACT FROM AUTHOR]

Although the cultivation of fruit trees is a traditional activity in Romania and our country has natural potential in this regard, both the cultivated area and the fruit production have decreased lately. That is why the NRDP 2014-2020 included a sub-programme for this sector, with sub-measures to redress the field and successfully respond to current challenges, including the impact on the environment. Today, growing concerns about the negative impact that agriculture has on the environment have led to the adoption of agricultural systems aimed at achieving sustainable yields. In this context, Expert System CROM ensures the sustainable development and the conservation of natural resources existing in tree plantations. The following study focuses on some research carried on the management of natural resources, using the CROM Expert System, in the field of apple orchards and lands. Apple orchards and farmlands were graded with additional points starting from 36 to 100 by implementing the Expert System CROM and were classified into three classes: unrestricted, with some restrictions and unsuitable for apple cultivation. [ABSTRACT FROM AUTHOR]

Apples attract humans with their dominant appeal and eating quality. The most dominant properties are color and texture. This review paper discusses the scientifically published results of apple texture through the understanding of internal properties like structural, physiological, physical, mechanical, water and cell wall contributions and post-harvest handling and storage methods. A critical appraisal is made of factors responsible for inner texture and sustenance during post-harvest and storage periods. The status of research data available is discussed from a firmness point of view of Indian apple varieties vis-à-vis those grown in other countries. Most of the research work on Indian apples reported physico-chemical parameters like weight, diameter, firmness, bio-chemical and nutrient components. Data for the long term in CAS (6 to 10 months) was not found in the latest literature. The research data on factors responsible for texture loss is also very limited for Indian-origin apples. There are still large gaps in research in finding firmness loss. Hence, suggestions are made on the scope for future research on apples grown in India for the benefit of farmers, traders, and ultimately the consumers. [ABSTRACT FROM AUTHOR]

As an effective tool for farmers to obtain agricultural information, the Internet has a profound impact on apple growers' perception of frost disasters and their adaptive behaviors. This paper constructed the theoretical analysis framework of "Apple growers' internet information acquisition-psychological perception-adaptive behavior to frost disaster", based on the data of 1020 apple growers in Shaanxi Province, empirically analyzing the impact of Internet information on apple growers' adaptive behaviors to frost disasters and its mechanism by the IV probit model and the intermediary effect model. The research found that the Internet information can significantly promote the apple growers' adaptive behaviors to frost disasters and has different effects on apple growers with different income levels and different management capabilities. Risk perception, effect perception, and risk attitudes have significant intermediary effects in the influence of the Internet information on apple growers' adaptive behaviors to frost disasters. Therefore, when governments take advantage of the Internet platform to help apple growers cope with frost disasters, they should fully consider the socio-economic conditions and psychological perception factors of apple growers and formulate targeted adaptation strategies. [ABSTRACT FROM AUTHOR]

China holds the top position globally in apple production and consumption. Detecting diseases during the planting process is crucial for increasing yields and promoting the rapid development of the apple industry. This study proposes a lightweight algorithm for apple leaf disease detection in natural environments, which is conducive to application on mobile and embedded devices. Our approach modifies the YOLOv8n framework to improve accuracy and efficiency. Key improvements include replacing conventional Conv layers with GhostConv and parts of the C2f structure with C3Ghost, reducing the model's parameter count, and enhancing performance. Additionally, we integrate a Global attention mechanism (GAM) to improve lesion detection by more accurately identifying affected areas. An improved Bi-Directional Feature Pyramid Network (BiFPN) is also incorporated for better feature fusion, enabling more effective detection of small lesions in complex environments. Experimental results show a 32.9% reduction in computational complexity and a 39.7% reduction in model size to 3.8 M, with performance metrics improving by 3.4% to a mAP@0.5 of 86.9%. Comparisons with popular models like YOLOv7-Tiny, YOLOv6, YOLOv5s, and YOLOv3-Tiny demonstrate that our YOLOv8n–GGi model offers superior detection accuracy, the smallest size, and the best overall performance for identifying critical apple diseases. It can serve as a guide for implementing real-time crop disease detection on mobile and embedded devices. [ABSTRACT FROM AUTHOR]

Apples (Malus domestica) are one of the most important temperate fruit crops in the world. Apple production provides, directly or indirectly, employment and improvement in the living standards to millions of people, playing a key role in rural economies. We have conducted an interview-based survey to assess the current status and challenges faced by apple production in the Batna and Khenchela provinces, the two most important apple producing regions of Eastern Algeria. The survey was conducted between March and November 2019. A total of 50 apple growers answered 40 questions, including 13 direct and 20 qualitative questions. A scale ranging from 1 (very low) to 5 (very high) was used as a quantitative measurement for 12 questions. The reliability index of the questionnaire was 0.827. The results identified the main challenges faced by apple growers in Eastern Algeria as: limited access to certified and clean planting material, limited knowledge of disease and pest identification, inadequate access to appropriate disease management, lack of storage facilities and transportation of harvested fruit, and insufficient knowledge of marketing and access to markets. The study also identified a need for the development of cost-effective plant multiplication, horticultural and disease management practices and educational programmes suitable for local conditions to enhance the productivity and economic benefits of apple orchards. [ABSTRACT FROM AUTHOR]

China leads the world in apple production, contributing to over half of the global total. Annually, extensive pesticide use is employed in apple cultivation, but the uncertain quantities of pesticide deposition and indiscriminate application have resulted in significant pesticide loss and residue. This poses a considerable potential risk to human health. This paper focuses on ground-based plant protection and stratifies the canopy horizontally, using 3D point cloud reconstruction technology and tree deposition experiments to explore the relationship between canopy leaf area index and pesticide deposition. By calculating the droplet penetration coefficient based on the amount of solution deposited at various canopy layers, a functional model linking leaf area index and droplet penetration coefficient has been established, with a correlation coefficient (R2) of 0.92. Based on this model, and by integrating the canopy volume with the amount of solution deposited on the outermost layer of the canopy, the total solution deposition on the entire tree can be estimated. According to the deposition model, there is a negative correlation between the amount of solution deposited on the canopy and the leaf area index of the canopy. Moreover, the experiments have shown that when the spray vehicle travels at a speed of 1.5 m/s, the maximum amount of solution is deposited while still ensuring an effective deposition rate. The establishment of the deposition estimation model allows for the visualization of canopy solution deposition and can guide fruit farmers in determining the appropriate frequency of spraying based on the severity of pest and disease infestations. [Display omitted] • Accurately forecasting the deposition of solutions on the canopy of apple trees. • Development of a model for solution penetration coefficient. • Establishing standard parameters for the optimal voxel size of apple trees. • Determining the optimal driving speed for pesticide trucks. [ABSTRACT FROM AUTHOR]

Apple is one of the most important fruit growing species from the northern hemisphere. Fruit quality is important for capitalization, which is why technologists are trying to ensure, using technology, the best possible conditions to produce qualitative apple fruits. The current research presents a comparison of fruit quality for four varieties: 'Florina', 'Generos', 'Red Topaz' and 'Redix', cultivated in three areas: Pitesti, Bucharest and Moara Domnească. Fruit quality was influenced by the culture area, but also largely by the climatic year. Fruit size had values within the limits of each variety for all three areas, but the location had a different influence on this parameter. Dry soluble substance had higher values recorded for the varieties 'Florina' and 'Redix' cultivated in Bucharest area, while the varieties 'Generos' and 'Red Topaz' for Pitesti area. Fruit firmness was better for the fruit obtained in Pite'ti area (hilly area), compared to the other two areas, Bucharest and Moara Domnească (lower areas), while the content in anthocyanins and polyphenols was influenced more by the culture conditions than by the varieties' characteristics. [ABSTRACT FROM AUTHOR]

Apple replant disease (ARD), caused by Fusarium pathogens, is a formidable threat to the renewal of apple varieties in China, necessitating the development of effective and sustainable control strategies. In this study, the bacterial strain BA-4 was isolated from the rhizosphere soil of healthy apple trees in a replanted orchard, demonstrating a broad-spectrum antifungal activity against five crucial apple fungal pathogens. Based on its morphology, physiological and biochemical traits, utilization of carbon sources, and Gram stain, strain BA-4 was tentatively identified as Bacillus amyloliquefaciens. Phylogenetic analysis using 16S rDNA and gyrB genes conclusively identified BA-4 as B. amyloliquefaciens. In-depth investigations into B. amyloliquefaciens BA-4 revealed that the strain possesses the capacity to could secrete cell wall degrading enzymes (protease and cellulase), produce molecules analogous to indole-3-acetic acid (IAA) and siderophores, and solubilize phosphorus and potassium. The diverse attributes observed in B. amyloliquefaciens BA-4 underscore its potential as a versatile microorganism with multifaceted benefits for both plant well-being and soil fertility. The extracellular metabolites produced by BA-4 displayed a robust inhibitory effect on Fusarium hyphal growth and spore germination, inducing irregular swelling, atrophy, and abnormal branching of fungal hyphae. In greenhouse experiments, BA-4 markedly reduced the disease index of Fusarium-related ARD, exhibiting protective and therapeutic efficiencies exceeding 80% and 50%, respectively. Moreover, BA-4 demonstrated plantpromoting abilities on both bean and Malus robusta Rehd. (MR) seedlings, leading to increased plant height and primary root length. Field experiments further validated the biocontrol effectiveness of BA-4, demonstrating its ability to mitigate ARD symptoms in MR seedlings with a notable 33.34% reduction in mortality rate and improved biomass. Additionally, BA-4 demonstrates robust and stable colonization capabilities in apple rhizosphere soil, particularly within the 10-20 cm soil layer, which indicates that it has long-term effectiveness potential in field conditions. Overall, B. amyloliquefaciens BA-4 emerges as a promising biocontrol agent with broad-spectrum antagonistic capabilities, positive effects on plant growth, and strong colonization abilities for the sustainable management of ARD in apple cultivation. [ABSTRACT FROM AUTHOR]

Apple is the most important fruit tree in West Azarbaijan province of Iran. In a survey of apple orchards, a disease with crown and collar canker and necrosis symptoms was observed in three young apple orchards in Urmia, affecting 15% and 1% of 'Red Delicious' and 'Golden Delicious' cultivars, respectively. A fungus with typical characteristics of the asexual morph of Cytospora was regularly isolated from the diseased tissues. Morphological characteristics and phylogenetic analyses inferred from the combined dataset of the ITS-rDNA, parts of LSU, tef1-α, rpb2, and act1 genes revealed that the isolates represent a new species of Cytospora, described herein as Cytospora balanejica sp. nov.. The pathogenicity of all isolates was confirmed on apple cv. 'Red Delicious' based on Koch's postulates. Also, the reaction of 12 other apple cultivars was assessed against five selected isolates with the highest virulence. The results showed that except for cv. 'Braeburn', which did not produce any symptoms of the disease, the other 11 cultivars showed characteristic disease symptoms including sunken and discolored bark and wood. The mean length of the discolored area was different among the 11 so-called susceptible cultivars, hence cvs. 'M4' and 'Golden Delicious' showed the highest and the lowest lesion length, respectively. Moreover, the aggressiveness of the five tested isolates was different, and the isolates BA 2-4 and BA 3-1 had the highest and lowest aggressiveness, respectively. Based on our observations on the potential ability of the fungus to cause disease on young and actively growing apple trees, it will be a serious threat to apple cultivation and industry. [ABSTRACT FROM AUTHOR]

Apple production holds a prominent position in Morocco's Rosaceae family. However, annual production can fluctuate due to substantial losses caused by fungal diseases affecting stored apples. Our findings emphasize that the pre-storage treatment of apples, disinfection of storage facilities, box type, and fruit sorting are pivotal factors affecting apple losses during storage. Additionally, the adopted preservation technique was significantly correlated with the percentage of damage caused by fungal infections. Blue mold accounts for nearly three-quarters of the diseases detected, followed by gray rot with a relatively significant incidence. This study has revealed several fungal diseases affecting stored apples caused by pathogens such as Penicillium expansum, Botrytis cinerea, Alternaria alternata, Trichothecium roseum, Fusarium avenaceum, Cadophora malorum, and Neofabraea vagabunda. Notably, these last two fungal species have been reported for the first time in Morocco as pathogens of stored apples. These data affirm that the high losses of apples in Morocco, attributed primarily to P. expansum and B. cinerea, pose a significant threat in terms of reduced production and diminished fruit quality. Hence, adopting controlled atmosphere storage chambers and implementing good practices before apple storage is crucial. [ABSTRACT FROM AUTHOR]

Despite being the highest value fruit crop in Australia, little is known about the types and condition or "health" of Australia's apple growing soils. This study is unique in being the first to report the condition and characteristics of Australia's apple growing soils; it provides essential baseline data for future monitoring of soil health in apple production systems, as well as soil physical and chemical data required for the development of perennial soil-tree-climate models. Soil chemical and physical properties were measured at 34 orchards, across five states. Soils were assessed for water retention, hydraulic conductivity, bulk density, macroporosity, organic carbon, CEC, ESP, pH, and EC. Despite high to very high levels of organic carbon, most topsoils were moderately to poorly structured. Around one-third to half of all sites showed evidence of poor aeration or impeded drainage, whilst 10 of the 34 sites were prone to nutrient leaching. Plant available soil water (PAWC) varied greatly between sites from 31 mm to 170 mm from 0 to 60 cm depth and between sites within the same soil order. Whilst topsoils had high to very high levels of organic carbon (average: 2.46%), they were otherwise poorly structured, with higher than expected bulk density (average: 1.32 g/cm3) and lower than expected air capacity (average: 9.97%) and macroporosity (average: 1.75%). Subsoils were also found to have little soil water availability (average: 15.39 mm/100 mm), low air capacity (average: 5.28%), and low CEC (average: 8.12 cmol (+) kg−1). Notably, 10 of the 34 sites had less than 6 cmol (+) kg−1 CEC throughout the entire soil profile, indicating potential risk of nutrient leaching. This study indicates that apple growing soils require careful management to improve topsoil structure, and to maintain or increase soil carbon, as well as use of soil moisture sensors to schedule irrigation. In addition, some sites also require improved subsoil drainage and care to ensure fertigation and irrigation do not result in leaching of nutrients beneath the root zone. [ABSTRACT FROM AUTHOR]

In apple cultivation, the total nitrogen content is an important indicator of plant growth, fruit quality, and yield. Timely monitoring of growth becomes imperative, since an imbalance, either in deficiency or excess nitrogen, can result in physiological disorders, adversely impacting both the quantity and quality of fruit. Leaf nitrogen content can be determined using simple chlorophyll meters or destructive testing; however, these methods are time-consuming. However, by employing spectral imaging technology, it is possible to swiftly predict leaf nitrogen content. This study estimated the total nitrogen content in apple trees via hyperspectral imaging and machine learning-based regression analysis (partial least-squares regression (PLSR), support vector regression (SVR), and eXtreme gradient boosting regression (XGBoost). Additionally, to reduce computational costs and improve reproducibility, spectral binning was divided into three stages (4, 8, and 16 bins), and models were compared with a 2-binning estimation model. The analysis focused on green, red, red edge, and near-infrared (NIR) spectra, with 5–10 selected wavelengths, and the SVR-based prediction model showed a similar or greater performance to that of the full spectrum. At 4- and 8-binning, the selected wavelengths were similar to those at 2-binning, maintaining similar prediction model performance. However, at 16 bp, the performance of the prediction model decreased owing to spectral data loss, leading to a significant reduction in wavelengths for nitrogen content estimation. These results can support informed nitrogen fertilization decisions, enabling precise, real-time monitoring of nitrogen content for enhanced plant growth, fruit quality, and yield in apple trees. Additionally, the selected wavelengths can be considered in the development of new types of multispectral sensors. [ABSTRACT FROM AUTHOR]

For molecular breeding of future apples, wild apple (Malus sieversii), the primary progenitor of domesticated apples, provides abundant genetic diversity and disease-resistance traits. Valsa canker (caused by the fungal pathogen Valsa mali) poses a major threat to wild apple population as well as to cultivated apple production in China. In the present study, we developed an efficient system for screening disease-resistant genes of M. sieversii in response to V. mali. An optimal agrobacterium-mediated transient transformation of M. sieversii was first used to manipulate in situ the expression of candidate genes. After that, the pathogen V. mali was inoculated on transformed leaves and stems, and 3 additional methods for slower disease courses were developed for V. mali inoculation. To identify the resistant genes, a series of experiments were performed including morphological (incidence, lesion area/length, fungal biomass), physiological (H2O2 content, malondialdehyde content), and molecular (Real-time quantitative Polymerase Chain Reaction) approaches. Using the optimized system, we identified two transcription factors with high resistance to V. mali, MsbHLH41 and MsEIL3. Furthermore, 35 and 45 downstream genes of MsbHLH41 and MsEIL3 were identified by screening the V. mali response gene database in M. sieversii, respectively. Overall, these results indicate that the disease-resistant gene screening system has a wide range of applications for identifying resistant genes and exploring their immune regulatory networks. [ABSTRACT FROM AUTHOR]

Apple proliferation, caused by 'Candidatus Phytoplasma mali', is one of the most important economic threats in the field of apple production. Especially at a young age, infected trees can be affected by excessive bud proliferation and general decline. The fruit quality is also significantly reduced by this disease. To investigate treatment options, we applied a clarithromycin chemotherapy to infected in vitro cultures of 'Golden Delicious'. With increasing concentrations of clarithromycin in the media, the phytoplasma load decreased rapidly after one month of treatment, but phytotoxicity led to a pronounced mortality at 40 mg/L, which was the highest dose used in our experiment. Out of 45 initial explants, we obtained one negative mericlone and two mericlones with a concentration of phytoplasma DNA at the detection limit of PCR. The culture propagated from the mericlone that tested negative remained phytoplasma-free after 18 months of subculturing. Our results suggest the applicability of macrolide antibiotics against phytoplasma infections in vitro; however, it might be challenging to find the threshold zone where the concentration is sufficient for pathogen elimination, but not lethal for the plant material of different cultivars. [ABSTRACT FROM AUTHOR]

Non-destructive assessment of fruits for grading and quality determination is essential to automate pre- and post-harvest handling. Near-infrared (NIR) hyperspectral imaging (HSI) has already established itself as a powerful tool for characterizing the quality parameters of various fruits, including apples. The adoption of HSI is expected to grow exponentially if inexpensive tools are made available to growers and traders at the grassroots levels. To this end, the present study aims to explore the feasibility of using a low-cost visible-near-infrared (VIS-NIR) HSI in the 386–1028 nm wavelength range to predict the moisture content (MC) and pH of Pink Lady apples harvested at three different maturity stages. Five different machine learning algorithms, viz. partial least squares regression (PLSR), multiple linear regression (MLR), k-nearest neighbor (kNN), decision tree (DT), and artificial neural network (ANN) were utilized to analyze HSI data cubes. In the case of ANN, PLSR, and MLR models, data analysis modeling was performed using 11 optimum features identified using a Bootstrap Random Forest feature selection approach. Among the tested algorithms, ANN provided the best performance with R (correlation), and root mean squared error (RMSE) values of 0.868 and 0.756 for MC and 0.383 and 0.044 for pH prediction, respectively. The obtained results indicate that while the VIS-NIR HSI promises success in non-destructively measuring the MC of apples, its performance for pH prediction of the studied apple variety is poor. The present work contributes to the ongoing research in determining the full potential of VIS-NIR HSI technology in apple grading, maturity assessment, and shelf-life estimation. [ABSTRACT FROM AUTHOR]

Apple is an important cash crop in China, and the prediction of its freshness can effectively reduce its storage risk and avoid economic loss. The change in the concentration of odor information such as ethylene, carbon dioxide, and ethanol emitted during apple storage is an important feature to characterize the freshness of apples. In order to accurately predict the freshness level of apples, an electronic nose system based on a gas sensor array and wireless transmission module is designed, and a neural network prediction model using an improved Sparrow Search Algorithm (SSA) based on chaotic sequence (Tent) to optimize Back Propagation (BP) is proposed. The odor information emitted by apples is studied to complete an apple freshness prediction. Furthermore, by fitting the relationship between the prediction coefficient and the input vector, the accuracy benchmark of the prediction model is set, which further improves the prediction accuracy of apple odor information. Compared with the traditional prediction method, the system has the characteristics of simple operation, low cost, reliable results, mobile portability, and it avoids the damage to apples in the process of freshness prediction to realize non-destructive testing. [ABSTRACT FROM AUTHOR]

Human perceptions and concerns about the implications of climate change have a profound importance for adaptation to and mitigation of the impacts of climate change at various scales and in different economic and social sectors in a given geographical landscape. Although studies on public perceptions of climate change are increasing globally, they are still scant pertaining to the Himalayan region of Nepal. In order to fill this gap, this study aimed to understand individual and community perceptions of climate change in the Lower Mustang region of Nepal, where apple farming and agricultural activities have been significantly impacted by climate change over the last 30–40 years. The data collection for the study included a cross-sectional survey of 200 farmers with a structured questionnaire, seven focus group discussions, and 26 key informant interviews, which were administered in four rural municipalities: Kagbeni, Jomsom, Marpha, and Tukuche. Results of the study indicated that a majority of the participants experienced an increase in temperature with warmer summers (99% of the participants surveyed), a decrease in snowfall (100%), and variable rainfall patterns (87%). However, the warming climate-enhanced diversification of vegetables in Lower Mustang, and the changes in climatic patterns over the years have affected almost all the agricultural systems of the region. In particular, apple farming in lower elevations has been negatively impacted due to decreasing snowfall, necessitating an urgent attention to develop sustainable adaptation strategies in order to minimize the deleterious impacts of the changing climatic patterns. [ABSTRACT FROM AUTHOR]

This study examines how consumers perceive agricultural products affected by natural disasters, using apples in Japan as a case study. Typhoons and other natural disasters frequently damage the surface of apples during the production season, causing significant harm to farmers' businesses, particularly when a large typhoon hits the production area. To maintain the sustainability of agricultural production, consumers need to purchase damaged crops at a certain price. To assess the effect of product attributes, such as appearance and price, and personal attributes of respondents, we conducted a choice-based conjoint analysis using a mixed logit model. The estimated results using the main effect and cross-section models show that consumers generally devalue apples when they have scratches. However, by using consumer co-operatives on a daily basis and disseminating simple information about the relationship between scratches and natural disasters, we could mitigate this devaluation tendency and thus contribute to sustainable agricultural production. [ABSTRACT FROM AUTHOR]

The lack of dwarf stock with good cold resistance has affected the production of apples in northern China. Annual dormant branches of 'Tianhong 2' apple were grafted on 10 different dwarf interstocks (the rootstocks were the seedlings of Malus hupehensis var. Pingyiensis) as test materials. Among these 10 interstocks, Huang 6, 244, NO.1, 53, 24-5, ZC9-3, Jizhen 1 were newly developed by us (Apple Research Group of Hebei Agricultural University), and three interstocks with different degrees of cold resistance (GM256–with strongest cold resistance, SH40–with stronger cold resistance, M9–with poor cold resistance) were used as controls. The semi-lethal temperature (LT50) and related physiological indexes of the branches in the overwintering process were studied. Based on the comprehensive physiological indexes, the effects of 10 interstocks on cold resistance of the 'Tianhong 2' apple were analyzed. The results showed that the effects of 10 kinds of interstocks on the cold resistance of 'Tianhong 2' apple were quite different. The order of effects on cold resistance from strong to weak was as follow: GM256 > Huang 6 > 244 >NO.1> 53 > 24-5 > ZC9-3 >Jizhen 1 > SH40 > M9. The purpose of this study was to screen out the interstocks with strong cold resistance, in order to provide some basis for the selection and utilization of interstocks with strong cold resistance in apple cultivation to further promote the development of the apple industry in China. [ABSTRACT FROM AUTHOR]

Changing the harvesting time of apples from the optimum harvest window to delayed harvesting may be applied if it is impossible to harvest apples at the optimal time, but it requires changing other factors, as they influence the quality of fruits and shelf life. The aim of the study was to assess the quality of 'Red Jonaprince' apples during storage after delayed harvesting and 1-methylcyclopropene (1-MCP) preharvest and postharvest treatment for various storage times. Apples were studied within four groups subjected to preharvest and postharvest treatments, as follows: Group 0—no 1-MCP treatment; Group 1—1-MCP preharvest treatment; Group 2—1-MCP postharvest treatment; and Group 3—1-MCP preharvest and postharvest treatment. All apples were subjected to ultra-low oxygen (ULO) storage conducted for 3, 5 or 6 months, while the analyses were conducted directly after ULO storage (simulated shelf life—0 days) and after simulated shelf life (7 days). For firmness, in the case of 1-MCP applied only preharvest (Group 1) and only postharvest (Group 2), before shelf life, the longer ULO storage resulted in obtaining lower values of firmness (p < 0.0001). If 1-MCP was not applied postharvest (Group 0 and Group 1), and short ULO storage was applied (3 and 5 months for Group 0; 3 months for Group 1), after shelf-life lower values of firmness were observed (p < 0.0001). For soluble solids content (SSC), in the case of 1-MCP not applied preharvest (Group 0 and Group 2), before shelf life, and for 1-MCP applied postharvest (Group 2) after shelf life, the longer ULO storage resulted in obtaining lower values of SCC (p < 0.0001). For titratable acidity (TA), in the case of all the studied groups after shelf life, as well as in case of 1-MCP applied only preharvest (Group 1) also before shelf life, the longer ULO storage resulted in obtaining lower values of TA (p < 0.0001). Except for the 1-MCP applied only postharvest (Group 2), in the case of short ULO storage applied (3 and 5 months for Group 0; 5 months for Group 1; 5 months for Group 3), after shelf-life lower values of TA were observed (p < 0.0001). If delayed harvesting must be conducted, applying 1-MCP not only postharvest, but also preharvest, allows obtaining the most stable firmness and SSC, which do not decrease during storage and shelf life. Taking this into account, it may be concluded, that in the case of delayed harvesting, combining 1-MCP applied preharvest and postharvest should be recommended to keep the quality parameters stable during storage and shelf life. [ABSTRACT FROM AUTHOR]

It is widely recognized that the quality of fruits and vegetables can be altered during transportation and storage. Firmness and loss of weight are the crucial attributes used to evaluate the quality of various fruits, as many other quality attributes are related to these two attributes. These properties are influenced by the surrounding environment and preservation conditions. Limited research has been conducted to accurately predict the quality attributes during transport and storage as a function of storage conditions. In this research, extensive experimental investigations have been conducted on the changes in quality attributes of four fresh apple cultivars (Granny Smith, Royal Gala, Pink Lady, and Red Delicious) during transportation and storage. The study evaluated the weight loss and change in firmness of these apples varieties at different cooling temperatures ranging from 2 °C to 8 °C to assess the impact of storing at these temperatures on the quality attributes. The results indicate that the firmness of each cultivar continuously decreased over time, with the R2 values ranging from 0.9489–0.8691 for red delicious, 0.9871–0.9129 for royal gala, 0.9972–0.9647 for pink lady, and 0.9964–0.9484 for granny smith. The rate of weight loss followed an increasing trend with time, and the high R2 values indicate a strong correlation. The degradation of quality was evident in all four cultivars, with temperature having a significant impact on firmness. The decline in firmness was found to be minimal at 2 °C, but increased as the storage temperature increased. The loss of firmness also varied among the four cultivars. For instance, when stored at 2 °C, the firmness of pink lady decreased from an initial value of 8.69 kg·cm2 to 7.89 kg·cm2 in 48 h, while the firmness of the same cultivar decreased from 7.86 kg·cm2 to 6.81 kg·cm2 after the same duration of storage. Based on the experimental results, a multiple regression quality prediction model was developed as a function of temperature and time. The proposed models were validated using a new set of experimental data. The correlation between the predicted and experimental values was found to be excellent. The linear regression equation yielded an R2 value of 0.9544, indicating a high degree of accuracy. The model can assist stakeholders in the fruit and fresh produce industry in anticipating quality changes at different storage stages based on the storage conditions. [ABSTRACT FROM AUTHOR]

Agricultural production is already, and obviously, affected by climate change. Adapting to climate change includes reducing future risks to ensure yield quality and quantity and considers seizing any potential opportunities induced by climate change. In higher latitude areas, such as Norway, cold climate limits the cultivation of fruits. An increase in temperature offers more favorable conditions for fruit production. In this study, using available phenological observations (full blooming) and harvest dates, and meteorological data from the experimental orchard of NIBIO Ullensvang, the minimum heat requirements for growing different apple varieties are determined. Those criteria are used for zoning of the areas with heat favorable conditions for apple growing. Data on six varieties were used, with lower and higher requirements for heat for fruit development (Discovery, Gravenstein, Summerred, Aroma, Rubinstep, and Elstar). High resolution daily temperature data were generated and used for zoning of the areas with heat favorable conditions for apple growing within the selected domain, which includes Western Norway, Southern Norway, Eastern Norway, and the western part of Trøndelag, Mid-Norway. Dynamics of the change in such surfaces was assessed for the period of 1961–2020. The total surface with favorable heat conditions for growing the varieties with lesser requirement for heat increased three times during this period. The growing of more heat-demanding varieties increased from near zero to about 2.5% of the studied land surface. In the period of 2011–2020, surface area with favorable heat conditions for apple growing was almost 27,000 km2, and a surface area of about 4600 km2 can sustain growing of more heat-demanding varieties. The presented results show the increasing potential of the climate of Norway for apple cultivation and highlight the importance of implementation of fruit production planned according to climate change trends, including the assessment of potential risks from climate hazards. However, the methodology for determining heat requirements can be improved by using phenological ripening dates if available, rather than harvest dates which are impacted by human decision. Zoning of areas with the potential of sustainable apple growing requires the use of future climate change assessments and information on land-related features. [ABSTRACT FROM AUTHOR]

The commercial apple production in Norway is limited to the small regions along the fjords in the southwest part of the country and around lakes or near the sea in the southeast with favorable climate. Due to the rapid rate of climate change over the recent decades, it is expected that suitable heat conditions for apple growing will expand to the areas that were previously too cold. This study analyses the heat suitability of future climate (2021–2100) under the RCP8.5 scenario for 6 common apple varieties in Norway: Discovery, Gravenstein, Summerred, Aroma, Rubinstep and Elstar. Previously established heat requirement criteria (based on the temperature threshold for the full blooming and growing degree days sum between the full bloom and harvest) are applied to the temperature outputs of the regional climate models downscaled to 1 km resolution. The assessment indicates that as temperature rises, heat conditions suitable for cultivation of all 6 apple varieties will expand. According to the ensemble median value, areas with the favorable heat conditions for growing at least one of the considered apple varieties will increase 25 times in the period 2021–2040 and 60 times in the period 2041–2060, compared to the referent period 1971–2000. At the same time, areas suitable for all 6 apple varieties will increase 3 times in the first, and 3.8 times in the latter period. The favorable areas will advance from south and southeast northwards and inland in the eastern region, along the west and northwestern coastline towards higher latitudes, and along continental parts of fjords. The fastest expansion of heat suitable conditions is expected for Discovery and Gravenstein. The findings of this study are relevant for zoning apple production future potential and for strategical planning of climate change adaptation measures within the sector. Weather-related risks, such as risks from winter low temperatures, spring frost, drought and extreme precipitation were not considered. [ABSTRACT FROM AUTHOR]

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Background: Apple stem grooving virus (ASGV) has a wide host range, notably including apples, pears, prunes and citrus. It is found worldwide. Method: In this study, two near complete genomes, and seven coat protein (CP) sequences of Iranian isolates from apple were determined. Sequences added from GenBank provided alignments of 120 genomic sequences (54 of which were recombinant), and 276 coat protein genes (none of them recombinant). Result: The non-recombinant genomes gave a well supported phylogeny with isolates from diverse hosts in China forming the base of the phylogeny, and a monophyletic clade of at least seven clusters of isolates from around the world with no host or provenace groupings among them, and all but one including isolates from China. The six regions of the ASGV genome (five in one frame, one − 2 overlapping) gave significantly correlated phylogenies, but individually had less statistical support. The largest cluster of isolates contained those from Iran and had isolates with worldwide provenances, and came from a wide range of mono- and dicotyledonous hosts. Population genetic comparisons of the six regions of the ASGV genome showed that four were under strong negative selection, but two of unknown function were under positive selection. Conclusion: ASGV most likely originated and spread in East Asia in one or more of various plant species, but not in Eurasia; the ASGV population of China had the greatest overall nucleotide diversity and largest number of segregating sites. [ABSTRACT FROM AUTHOR]